Scientific Research Publishing

Anomaly Detection in Selected Aerosol Optical Properties and Associated Climate Variables Using a Multivariate Hidden Markov Model: A Case Study over Kenya ()

1 Department of Science, Technology and Engineering, Kibabii University, Bungoma, Kenya. 2 Department of Renewable Energy and Technology, Turkana University College, Lodwar, Kenya. Understanding ...
Scientific Research Publishing

Machine Learning-Based Outlier Detection in Long-Term Climate Data: Evidence from Burkina Faso’s Synoptic Network ()

In recent decades, the impact of climate change on natural resources has increased. However, the main challenges associated with the collection of meteorological data include the presence of missing, ...
SciELO

EVALUATION OF THE QUALITY AND CLASSIFICATION OF PARBOILED RICE USING NEAR-INFRARED SPECTROSCOPY AND MULTIVARIATE STATISTICAL ANALYSES

Physical classification is the official standard method for determining grain quality for commercialization. However, it is a time-consuming, subjective operation, susceptible to errors, and requires ...
IEEE

Variable-Dynamic Multivariate Time Series Forecasting for IoT Systems

Abstract: The past decade has witnessed the success of deep learning-based multivariate time series forecasting in Internet of Things (IoT) systems. However, dynamic variable correlation remains a ...
GitHub

Multivariate analysis of ordinal data with missing values and latent variables

Halva—‘grapHical Analysis with Latent VAriables’—is a Python package dedicated to statistical analysis of multivariate ordinal data, designed specifically to handle missing values and latent variables ...
JSTOR Daily

Sparse abnormality detection based on variable selection for spatially correlated multivariate process

Monitoring the manufacturing process becomes a challenging task with a huge number of variables in traditional multivariate statistical process control (MSPC) methods. However, the rich information is ...
IEEE

Fisher Discriminant Analysis for Extracting Interpretable Phenological Information From Multivariate Time Series Data

Abstract: This article explores the use of Fisher discriminant analysis (FDA) as a method for extracting time-resolved information from multivariate environmental time series data. FDA is useful ...
chromatographyonline

Multivariate Design of Experiments for Gas Chromatographic Analysis

Recent advances in green chemistry have made multivariate experimental design popular in sample preparation development. This approach helps reduce the number of measurements and data for evaluation ...